Essays on intergenerational income mobility, geographical mobility, and education

• Main Author: Heidrich, Stefanie
• Format: Doctoral Thesis
• Language: English
• Published: Umeå universitet, Nationalekonomi 2016
• Subjects: optimal taxation; social norms; intergenerational mobility; regional analysis; multilevel model; child human capital development; moving costs; simulation of income paths; lifetime income
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120718; http://nbn-resolving.de/urn:isbn:978-91-7601-440-0

This thesis consists of an introductory part and the following four self-contained papers: In Paper [I] we analyze the implications of social identity and self-categorization for optimal redistributive income taxation. A two-type model is supplemented by an assumption that individuals select themselves into social categories, in which norms are formed and education effort choices partly depend on these norms. The results show, among other things, that externality correction by a welfarist government leads to an element of tax progression that serves to reduce the discrepancy between the effort norm and the actual effort chosen by low-productivity individuals in the high-effort group. Furthermore, if the preference for social identity is sufficiently strong, increased wage-inequality leads to higher social welfare through a relaxation of the selection constraint. It may thus be desirable to use publicly provided education to induce more wage-inequality, even if higher wage-inequality increases the intrinsic utility of a potential mimicker. In Paper [II] I employ high quality register data to present new facts about income mobility in Sweden. The focus of the paper is regional differences in mobility, using a novel approach based on a multilevel model. This method is well-suited when regions differ greatly in population size as is the case in Sweden. The maximum likelihood estimates are substantially more precise than those obtained by running separate OLS regressions. I find small regional differences in income mobility when measured in relative terms. Regional differences are large when adopting an absolute measure and focusing on children with below-median parent income. On the national level I find that the association between parent and child income ranks has decreased over time, implying increased mobility. In Paper [III] I study the long term effects of inter-municipal moving during childhood on income using Swedish register data. Due to the richness of the data I am able to control for important sources of selection into moving, such as parent separation, parents' unemployment, education, long run income, and immigration background. I find that children's long run incomes are significantly negatively affected by moving during childhood, and the effect is larger for those who move more often. For children who move once, I also estimate the effect of the timing and the quality of the move. I measure the quality of each neighborhood based on the adult outcomes for individuals who never move. The quality of a move is defined as the difference in quality between the origin and the destination. Given that a family moves, I find that the negative effect of childhood moving on adult income is increasing in age at move. Children benefit economically from the quality of the region they move to only if they move before age 12 (sons) and age 16 (daughters). In Paper [IV] I study the bias of IGE estimates for different missing-data scenarios based on simulated income processes. Using an income process from the income dynamics and risks literature to generate two linked generations’ complete income histories, I use Monte Carlo methods to study the relationship between available data patterns and the bias of the IGE. I find that the traditional approach using the average of the typically available log income observations leads to IGE estimates that are around 40 percent too small. Moreover, I show that the attenuation bias is not reduced by averaging over many father income observations. Using just one income observation for each generation at the optimal age (as discussed in the paper) or using weighted instead of unweighted averages can reduce the bias. In addition, the rank-rank slope is found to be clearly less sensitive to missing data.